corpus-report

# Corpus Report Report on the continuous PDF extraction learning system running on the 12TB drive. ## Quick Start ```bash ./run.sh # Quick summary (default) ./run.sh quality # S00/S04 ratio analysis ./run.sh patterns # Failure pattern registry ./run.sh bottlenecks --sample 20 # Pipeline stage timing ./run.sh trends --since 24 # Quality over last 24 hours ``` ## Commands | Command | Description | |---------|-------------| | `summary` | Total/completed/pending/failed, categories, presets, label distribution | | `quality` | S00/S04 ratio histogram, worst offenders, accuracy percentages | | `patterns` | Failure pattern frequency, affected files, cross-referenced with categories | | `bottlenecks` | Per-stage timing aggregation, % of total pipeline time | | `trends` | Quality metrics grouped by time windows | ## Options | Flag | Commands | Description | |------|----------|-------------| | `--json` | All | Machine-readable JSON output | | `--category TEXT` | All except bottlenecks | Filter by PDF category (arxiv, standards, etc.) | | `--top N` | quality | Number of worst offenders (default: 10) | | `--sample N` | bottlenecks | Limit result directory scanning | | `--since HOURS` | trends | Only include recent PDFs | | `--window HOURS` | trends | Time window size (default: 6) | ## Environment | Variable | Default | Description | |----------|---------|-------------| | `CORPUS_ROOT` | `/mnt/storage12tb/extractor_corpus` | Corpus root directory | ## Memory + Taxonomy Integration The skill integrates with the shared memory and taxonomy systems via `memory_integration.py` for longitudinal quality tracking: - **Pre-hook (`recall_prior_reports`)**: Before generating a report, recalls prior corpus snapshots for trend comparison. Enables agents to detect quality drift over time without re-scanning. - **Post-hook (`learn_report`)**: After generating the summary, stores a corpus report snapshot (total PDFs, success rate, failure patterns, top issues) to memory with taxonomy bridge tags. - **Bridge keywords**: Precision, Resilience, Fragility, Corruption, Loyalty, Stealth (tuned to corpus quality domain). - **Tags**: `["corpus_report", "drift_tracking"] + bridges` Gracefully degrades if `common.memory_client` or `taxonomy/taxonomy.py` are unavailable. ## File Structure ``` corpus-report/ SKILL.md # This file run.sh # Shell entry point sanity.sh # Sanity checks memory_integration.py # Memory + Taxonomy hooks pyproject.toml # Dependencies corpus_report/ # Python package __init__.py __main__.py cli.py # Typer CLI config.py # Paths and constants formatters.py # Rich/JSON output manifest.py # Manifest loading and analysis models.py # Data models patterns.py # Failure pattern analysis timings.py # Pipeline timing analysis ``` ## Data Sources - `metadata/manifest.jsonl` -- Per-PDF status, metrics, quality labels - `metadata/pattern_registry.json` -- Detected failure patterns - `results/*/timings_summary.json` -- Per-stage pipeline timing ## Visualization After generating reports (especially with `--json`), offer to visualize via `/create-figure`: ```bash # Quality trends over time create-figure metrics --input corpus.json --output quality-trend.png --type line --title "Extraction Quality" # Pipeline bottleneck breakdown create-figure metrics --input corpus.json --output bottlenecks.png --type hbar --title "Pipeline Stage Timing" # Failure pattern distribution create-figure metrics --input corpus.json --output failures.png --type pie --title "Failure Patterns" ``` **When to offer:** After presenting quality or bottleneck data, ask: "Want me to chart the trends?"